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Record W4412052697 · doi:10.1101/2025.07.02.25330433

Brain-Controlled Epidural Spinal Stimulation for Upper-Limb Motor Function after Tetraplegia

2025· preprint· en· W4412052697 on OpenAlexaff
Atsushi Sasaki, Rizaldi Ahmad Fadli, Akiko Yuasa, Zachary Boogaart, Hiroki Saito, Nevena Musikic, Roberto de Freitas, Nicolò Macellari, Kevin Davis, Nilanjana Datta, Guilherme Santos Piedade, Seth Tigchelaar, Michael E. Ivan, W. Dalton Dietrich, Naaz Kapadia, Vera Zivanovic, Miloš R. Popović, Elvira Pirondini, Jonathan Jagid, Abhishek N. Prasad, James D. Guest, Marco Capogrosso, Joacir Graciolli Cordeiro, Matija Milosevic

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsTetraplegiaPhysical medicine and rehabilitationStimulationNeuroscienceUpper limbMedicinePsychologySpinal cordSpinal cord injury

Abstract

fetched live from OpenAlex

Abstract Spinal cord injury (SCI) disrupts descending motor pathways, leaving individuals with tetraplegia dependent on residual neural connections to generate voluntary movement, with limited recovery despite extensive rehabilitation. Epidural spinal cord stimulation (ESCS) has emerged as a promising neuromodulation strategy that can amplify spinal sensorimotor pathways, enabling residual circuits to respond more effectively to attempted voluntary commands. However, most approaches deliver stimulation continuously rather than in response to volitional intent, limiting the integration of cortical commands and spinal activation that may enhance both neuroprosthetic utility and the potential for recovery. Here, we present an implantable brain-computer interface (BCI) that decodes attempted movement from electrocorticography signals to trigger cervical ESCS during upper-limb motor tasks in an individual with chronic, motor-complete cervical SCI. We demonstrated that both BCI-driven and tonic ESCS immediately enhanced motor function compared to no stimulation, with BCI-ESCS producing greater improvements in grip force and reaching accuracy. Parallel to these assistive effects, BCI-ESCS facilitated corticospinal and spinal excitability after a single session, whereas tonic stimulation did not, suggesting the utility of BCI-driven neuromodulation for activity-dependent plasticity. Four weeks of BCI-ESCS use further drove meaningful improvements in hand motor function exceeding clinical improvement thresholds, with selected gains persisting at one-month follow-up. Together, these findings establish a translational proof-of-concept for an implantable BCI-ESCS, demonstrating the feasibility of intent-driven neuromodulation as a restorative strategy that provides both neuroprosthetic assistance and therapeutic benefit following chronic complete tetraplegia. One-Sentence Summary Implanted brain-controlled spinal stimulation provides neuroprosthetic assistance and drives recovery in chronic tetraplegia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.379
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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